Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
Computer modeling
Machine learning unlocks secrets to advanced alloys
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
Making climate models relevant for local decision-makers
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
Scientists use generative AI to answer complex questions in physics
A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.
To build a better AI helper, start by modeling the irrational behavior of humans
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
Using deep learning to image the Earth’s planetary boundary layer
Lincoln Laboratory researchers are using AI to get a better picture of the atmospheric layer closest to Earth’s surface. Their techniques could improve weather and drought prediction.
MIT-derived algorithm helps forecast the frequency of extreme weather
The new approach “nudges” existing climate simulations closer to future reality.
Engineering household robots to have a little common sense
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
Generative AI for smart grid modeling
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
To excel at engineering design, generative AI must learn to innovate, study finds
AI models that prioritize similarity falter when asked to design something completely new.